Competitors: We evaluate FABBOO against two recent state-of-the-art fairness-aware stream classifiers [1]}, [2]}, the fairness-agnostic non-stationary OSBoost [3]} and imbalance-aware non-stationary and fairness agnostic CSMOTE [4]}. We also employ two variations of FABBOO to show the impact of its different components, namely class imbalance and cumulative fairness. All methods employ AHTs [5]} as weak learners and therefore are able to handle concept drifts. The only exception are FAHT [2]} which is an incremental Hoeffding Tree that does not tackle concept drifts and CSMOTE [4]} which is build on top of adaptive random forest equipped with ADWIN to handle concept drifts. An overview follows:
